Magnetic resonance fingerprinting (MRF) is a recently proposed technique for multi-parametric quantitative magnetic resonance imaging (MRI). In MRF, by using a specifically designed magnetic resonance (MR) sequence, for different tissue types with different MR physiological parameters such as T1 (the longitudinal relaxation time) and T2 (the transverse relaxation time), distinctive signal evolutions (fingerprints) are generated. The unique signals can then be processed to recover the tissue types, with simultaneous estimation of multiple parameters such as T1 and T2.
The MRF process can be divided into two steps: acquiring signal evolutions and estimating tissue parameters. For the signal acquisition, to achieve the prerequisite that different tissue types will generate distinctive signals, currently the MRF sequences use empirically randomized or pseudorandomized flip angles (FA) and repetition times (TR) without optimization. On the other hand, several studies have implied that different sequences offer different levels of the final parameter estimation accuracy.
In the currently practiced MRF reconstruction processes, dictionary matching is used for the tissue parameter estimation. A dictionary is first built for a specific MR sequence and a set of possible tissue parameters. Each entry in the dictionary is generated by Bloch equation simulation, or other simulation methods such as extended phase graphs, of the signal of a certain tissue type under the MR sequence excitation (MR signal response). The signal from one voxel of the acquired images is then searched in the dictionary to find the closest match. And the matched tissue (with the multiple parameters) is assigned directly to the spatial location. The dictionary matching is subject to quantization error since only a finite number of tissues are included in the dictionary. The dictionary matching is also a time consuming process and will become a more serious issue as iterative reconstruction, finer gridded parameter space, and more complex physiological models are used. Further, aliasing artifacts due to under-sampling in the Fourier space and noise in the Fourier coefficients require the matching to be robust.
Accordingly, there is a need in the art for improved MRF reconstruction process.